Operational Methodologies & Playbooks

The Influence Architecture Workflow

A 7-step blueprint for building human-machine collaboration:

Capture As-Is State: Map every process step, input, output, and decision point. Validate roles and responsibilities.
Identify AI-Enablement Points: Locate bottlenecks, repetitive tasks, or decision points where data analysis can guide human choice.
Single Out Tasks: Clearly allocate specific functions to either humans or AI to avoid role duplication and ambiguity.
Define Human-AI Interaction: Design the interface—how users input data and how they receive/process AI outputs.
Define Triggers & Events: Establish time-based (e.g., monthly) or data-driven (e.g., threshold alerts) triggers for AI intervention.
Feedback Loops: Create mechanisms to evaluate performance, enabling the algorithm to learn and the human to provide course correction.
Embrace Evolution: Continuously refactor the blueprint. It is not static; it must evolve with organizational growth.

A visual tool embedded in dashboards to ensure data literacy and transparency.

The Problem: Complex visualizations can be misleading without context.
The Solution: A "Compass" (e.g., a gauge, bullseye, or status bar) that instantly tells the user the maturity level of the data:
- Descriptive (Past)
- Diagnostic (Why)
- Predictive (Future)
- Prescriptive (Action)
Benefit: Prevents users from mistaking a raw data report for a prescriptive recommendation.

A methodology to fix the "Thin Wide Line" disconnect between business and tech teams.

Brevity: Must fit on one sheet to force focus.
Visuals: Use graphics/illustrations, as they are easier to recall than text.
Interactive: Participants should write, draw, and circle items on the sheet during the meeting.